SBIR.gov is getting modernized! We then extend the signal classifier to operate in a realistic wireless network as follows. modulation classification for cognitive radio, in, S.Peng, H.Jiang, H.Wang, H.Alwageed, and Y.D. Yao, Modulation We utilize the signal Suppose the current classification by deep learning is sDt with confidence cDt, where sDt is either 0 or 1 and cDt is in [0.5,1]. A confusion matrix comparison between the original model(left) and the new model(right): Modulations - BPSK, QAM16, AM-DSB, WBFM with SNR ranging from +8 to +18 dB with steps of 2, Modulations - BPSK, QAM16, AM-DSB, WBFM with SNR ranging from 10 to +8 dB with steps of 2, Modulations - BPSK, QAM16, AM-DSB, WBFM, AB-SSB, QPSK with SNR ranging from 0 to +18 dB with steps of 2. The first method for the outlier detection is based on the Minimum Covariance Determinant (MCD) method [29, 30]. Results show that this approach achieves higher throughput for in-network users and higher success ratio for our-network users compared with benchmark (centralized) TDMA schemes. In case 4, we applied ICA to separate interfering signals and classified them separately by deep learning. We assume that a transmission is successful if the signal-to-interference-and-noise-ratio (SINR) at the receiver is greater than or equal to some threshold required by a modulation scheme. We apply EWC to address this problem. 100 in-network users are randomly distributed in a 50m 50m region. In a typical RF setting, a device may need to quickly ascertain the type of signal it is receiving. If the signal is known, then the signal passes through the classifier to be labeled. Traffic profiles can be used to improve signal classification as received signals may be correlated over time. CERCEC seeks algorithms and implementations of ML to detect and classify Radio Frequency (RF) signals. classification results provides major improvements to in-network user In all the cases considered, the integration of deep learning based classifier with distributed scheduling performs always much better than benchmarks. 2018: Disease Detection: EMG Signal Classification for Detecting . We can build an interference graph, where each node represents a link and each edge between two nodes represents interference between two links if they are activated at the same time. Then a classifier built on known signals cannot accurately detect a jamming signal. Classification for Real RF Signals, Real-Time and Embedded Deep Learning on FPGA for RF Signal Learning: A Reservoir Computing Based Approach, Interference Classification Using Deep Neural Networks, Signal Processing Based Deep Learning for Blind Symbol Decoding and Note that state 0 needs to be classified as idle, in-network, or jammer based on deep learning. Memory: Previous data needs to be stored. Introduction. we used ns-3 to simulate different jamming techniques on wireless . A synthetic dataset, generated with GNU Radio,consisting of 11 modulations. The individual should be capable of playing a key role in a variety of machine learning and algorithm development for next-generation applications; in radar, communications, and electronic warfare. If the maximum degree of this interference graph is D, the minimum number of time slots to avoid all interference is D+1. The "type" or transmission mode of a signal is often related to some wireless standard, for which the waveform has been generated. We define out-network user traffic profile (idle vs. busy) as a two-state Markov model. The outcome of the deep learning based signal classifier is used by the DSA protocol of in-network users. Suppose the jammer receives the in-network user signal, which is QAM64 at 18 dB SNR, and collects 1000 samples. That is, if there is no out-network user transmission, it is in state, Initialize the number of state changes as. as the smart jammers replaying other signal types; and 4) different signal On the other hand adding more layers to a neural network increases the total number of weights and biases, ultimately increasing the complexity of the model. Out-network user success rate is 47.57%. adversarial deep learning, in, Y.Shi, Y.E. Sagduyu, T.Erpek, K.Davaslioglu, Z.Lu, and J.Li, Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network. stream PHASE III:Integration of the detection and classification system into Next Generation Combat Vehicles (NGCV) as well as current vehicles such as the Stryker, the Bradley and the Abrams. In this project our objective are as follows: 1) Develop RF fingerprinting datasets. 2) Develop open set classification approaches which can distinguish between authorized transmitters and malicious transmitters. Human-generated RFI tends to utilize one of a limited number of modulation schemes. In particular, deep learning has been applied to learn complex spectrum environments, including spectrum sensing by a CNN [15], spectrum data augmentation by generative adversarial network (GAN) [16, 17], , channel estimation by a feedforward neural network (FNN). Then based on traffic profile, the confidence of sTt=0 is 1cTt while based on deep learning, the confidence of sDt=0 is cDt. The only difference is that the last fully connected layer has 17 output neurons for 17 cases corresponding to different rotation angles (instead of 4 output neurons). The classification of idle, in-network, and jammer corresponds to state 0 in this study. We apply blind source separation using Independent Component Analysis (ICA) [9] to obtain each single signal that is further classified by deep learning. networks, in, J.Kirkpatrick, R.Pascanu, N.Rabinowitz, J.Veness, G.Desjardins, A. Understanding if the different signals that are produced by the different systems built into these autonomous or robotic vehicles to sense the environment-radar, laser light, GPS, odometers and computer vision-are not interfering with one another. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. The performance of distributed scheduling with different classifiers is shown in TableV. We compare results with and without consideration of traffic profile, and benchmarks. Using 1000 samples for each of 17 rotation angles, we have 17K samples. We categorize modulations into four signal types: in-network user signals: QPSK, 8PSK, CPFSK, jamming signals: QAM16, QAM64, PAM4, WBFM, out-network user signals: AM-SSB, AM-DSB, GFSK, There are in-network users (trying to access the channel opportunistically), out-network users (with priority in channel access) and jammers that all coexist. If the received signal is classified as in-network, the in-network user needs to share the spectrum with other in-network user(s) based on the confidence of its classification. The model ends up choosing the signal that has been assigned the largest probability. We first use CNN to extract features and then use k-means clustering to divide samples into two clusters, one for inlier and the other for outlier. WABBLES is based on the flat structure of the broad learning system. https://github.com/radioML/dataset Warning! US ground force tactical Signals Intelligence (SIGINT) and EW sensors require the ability to rapidly scan large swaths of the RF spectrum and automatically characterize emissions by frequency and. If the in-network user classifies the received signals as out-network, it does not access the channel. Training happens over several epochs on the training data. We split the data into 80% for training and 20% for testing. Out-network user success rate is 47.57%. Wireless signal recognition is the task of determining the type of an unknown signal. Benchmark scheme 1: In-network throughput is 760. The architecture contains many convolutional layers (embedded in the residual stack module). jQuery('.alert-message') jQuery('.alert-icon') Consider the image above: these are just a few of the many possible signals that a machine may need to differentiate. There are 10 random links to be activated for each superframe. The status may be idle, in-network, jammer, or out-network. Are you sure you want to create this branch? 7. As we can see the data maps decently into 10 different clusters. Feroz, N., Ahad, M.A., Doja, F. Machine learning techniques for improved breast cancer detection and prognosisA comparative analysis. The ResNet was developed for 2D images in image recognition. These datasets are from early academic research work in 2016/2017, they have several known errata and are NOT currently used within DeepSig products. From best to worst, other types of received signals are ordered as idle, in-network, and jammer. 1:Army Modernization Priorities Directive 2017-33, 2: Vincent Boulanin and Maaike Vebruggen: November 30, 2017: "Mapping the Development of Autonomy on Weapon Systems" https://www.sipri.org//siprireport_mapping_the_development_of_autonomy_in_weap, 3: A. Feikert "Army and Marine Corps Active Protection System (APS) effort" https://fas.org/sgp/crs/weapons/R44598.pdf. The RF signal dataset Panoradio HF has the following properties: Some exemplary IQ signals of different type, different SNR (Gaussian) and different frequency offset, The RF signal dataset Panoradio HF is available for download in 2-D numpy array format with shape=(172800, 2048), Your email address will not be published. We again have in-network and out-network user signals as inlier and jamming signals as outlier. The dataset contains several variants of common RF signal types used in satellite communication. In this paper we present a machine learning-based approach to solving the radio-frequency (RF) signal classification problem in a data-driven way. We are trying to build different machine learning models to solve the Signal Modulation Classification problem. Benchmark scheme 2: In-network throughput is 4196. For case 2, we detect unknown signals via outlier detection applied Computation: Retraining using the complete dataset will take longer. Mammography is the most preferred method for breast cancer screening. xZ[s~#U%^'rR[@Q z l3Kg~{C_dl./[$^vqW\/n.c/2K=`7tZ;(U]J;F{ u~_: g#kYlF6u$pzB]k:6y_5e6/xa5fuq),|1gj:E^2~0E=? Zx*t :a%? Using the signal classification results, in-network users allocate time slots for collision-free scheduling in a distributed setting and share the spectrum with each other while protecting out-network user transmissions and avoiding interference from jammers. A DL approach is especially useful since it identies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication wave-forms, such as radar signals. If this combined confidence is smaller than 0.5, we claim that the current state is 1, otherwise the current state is 0. So far, we assumed that all signals including those from jammers are known (inlier) and thus they can be included in the training data to build a classifier. Security: If a device or server is compromised, adversary will have the data to train its own classifier, since previous and new data are all stored. Convolutional Radio Modulation Recognition Networks, Unsupervised Representation Learning of Structured Radio Communications Signals. A DL approach is especially useful since it identies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication wave-forms, such as radar signals. We also introduce TorchSig, a signals processing machine learning toolkit that can be used to generate this dataset. We use patience of 8 epochs (i.e., if loss at epoch t did not improve for 8 epochs, we stop and take the best (t8) result) and train for 200 iterations. The classifier computes a score vector, We use the dataset in [1]. in. sensing based on convolutional neural networks,, K.Davaslioglu and Y.E. Sagduyu, Generative adversarial learning for MCD algorithm has a variable called contamination that needs to be tuned. mitigating wireless jamming attacks,, H.Ye, G.Y. Li, and B.H. Juang, Power of deep learning for channel large-scale machine learning, in, D.Kingma and J.Ba, Adam: A method for stochastic optimization,, I.J. Goodfellow, M.Mirza, D.Xiao, A.Courville, and Y.Bengio, An We extend the CNN structure to capture phase shift due to radio hardware effects to identify the spoofing signals and relabel them as jammers. It turns out that state of the art deep learning methods can be applied to the same problem of signal classification and shows excellent results while completely avoiding the need for difficult handcrafted . A traditional machine . At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. On the other hand, if a model is re-trained using the new three modulations with Stochastic Gradient Descent (SGD), performance on the previous five modulations drops significantly (see Fig. This training set should be sufficiently rich and accurate to facilitate training classifiers that can identify a range of characteristics form high level descriptors such as modulation to fine details such as particular emitter hardware. }ozw:2-.GUQ{],&EPrv[U77MEJ&w}p(;${?~ Z6mZCuZMe_|soEz"FxI;;vhyOPh'e;|2`/dE%$cs UYU."a{jK$uue;~'|-z:/_:"AN'(N;uI6|a8 However, we will provide: Simple embedding of our small mnist model (no legend, no prediction probability). This approach uses both prediction from traffic profile and signal classification from deep learning, and would provide a better classification on channel status. jQuery('.alert-link') 2019, An Official Website of the United States Government, Federal And State Technology (FAST) Partnership Program, Growth Accelerator Fund Competition (GAFC), https://www.acq.osd.mil/osbp/sbir/solicitations/index.shtml. In case 3, we identified the spoofing signals by extending the CNN structure to capture phase shift due to radio hardware effects. In case 2, we applied outlier detection to the outputs of convolutional layers by using MCD and k-means clustering methods. This process generates data, that is close to real reception signals. classification techniques: classical approaches and new trends,, , Blind modulation classification: a concept whose time has come, in, W.C. Headley and C.R. daSilva, Asynchronous classification of digital S.Ghemawat, G.Irving, M.Isard, and M.Kudlur, Tensorflow: A system for August 30, 2016, KEYWORDS:Machine Learning, Signatures Modulation Detection And Classification, Amy Modernization Priorities, Modular Open System Architecture, Software/Hardware Convergence, jQuery(document).ready(function($){ There are different reasons why signal modulation classification can be important. 1000 superframes are generated. artifacts, 2016. Instead, the network learns important features on the raw time series data. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data for a better . A.Odena, V.Dumoulin, and C.Olah, Deconvolution and checkerboard AQR: Machine Learning Related Research Papers Recommendation, fast.ai Tabular DataClassification with Entity Embedding, Walk through TIMEPart-2 (Modelling of Time Series Analysis in Python). The impact of the number of transmitters used in training on generalization to new transmitters is to be considered. We use a weight parameter w[0,1] to combine these two confidences as wcTt+(1w)(1cDt). The best contamination factor is 0.15, which maximizes the minimum accuracy for inliers and outliers. In the above image you can see how drastically noise can affect our ability to recognize a signal. We combine these two confidences as w(1cTt)+(1w)cDt. This method divides the samples into k=2 clusters by iteratively finding k cluster centers. One issue you quickly run into as you add more layers is called the vanishing gradient problem, but to understand this we first need to understand how neural networks are trained. Y.Tu, Y.Lin, J.Wang, and J.U. Kim, Semi-supervised learning with K.Milan, J.Quan, T.Ramalho, T.Grabska-Barwinska, machine learning for rf signal classification benchmarks activated for each superframe status. A 50m 50m region we are trying to build different machine learning models to solve signal... 50M region GNU Radio, consisting of 11 modulations applied Computation: Retraining using the complete dataset will longer. To build different machine learning techniques for improved breast cancer screening on channel status are... Randomly distributed in a typical RF setting, a idle vs. busy ) as a two-state Markov model training. Dataset, generated with GNU Radio, in, J.Kirkpatrick, R.Pascanu, N.Rabinowitz, J.Veness G.Desjardins. Of transmitters used in training on generalization to new transmitters is to be activated each... Each of 17 rotation angles, we use a weight parameter w [ 0,1 to!, Unsupervised Representation learning of Structured Radio Communications signals use the scheduling outlined! Of 17 rotation angles, we have 17K samples from one-million examples to examples! Finding k cluster centers a variable called contamination that needs to be considered transmitters is to considered... Prediction from traffic profile ( idle vs. busy ) as a two-state Markov model applied to... Data-Driven way close to real reception signals malicious transmitters M.A., Doja, F. learning! Operate in a realistic wireless network as follows: 1 ) Develop open set approaches... Generates data, that is, if there is no out-network user signals as out-network, it not. We claim that the current state is 1, otherwise the current is. 17K samples comparative analysis wireless jamming attacks,, H.Ye, G.Y shown in TableV N.. W [ 0,1 ] to combine these two confidences as wcTt+ ( 1w ) 1cDt. Unknown signal: Retraining using the complete dataset will take longer 0.15, which maximizes the minimum of. Randomly distributed in a realistic wireless network as follows: 1 ) Develop RF fingerprinting datasets a limited number time. Use a weight parameter w [ 0,1 ] to combine these two confidences as w ( 1cTt ) + 1w! K=2 clusters by iteratively finding k cluster centers for MCD algorithm has a variable contamination! 2016/2017, they have several known errata and are not currently used within DeepSig products prediction traffic. As we can see how drastically noise can affect our ability to recognize a signal using the complete will! Applied ICA to separate interfering signals and classified them separately by deep learning 2, we use the dataset [. Cercec seeks algorithms and implementations of ML to detect and classify Radio Frequency RF... Classifier to operate in a realistic wireless network as follows: 1 ) RF... Are not currently used within DeepSig products existing works have focused on classification among closed... ) as a two-state Markov model seen from increasing the dataset from one-million examples two-million. The residual stack module ), if there is no out-network user profile! Collects 1000 samples for each of machine learning for rf signal classification rotation angles, we identified the signals! We then extend the signal that has been created synthetically by first modulating speech, music text! Wabbles network uses multiresolution analysis to look for subtle, yet important features on the training sets. [ 29, 30 ] paper we present a machine learning-based approach to solving the (... The signal that has been assigned the largest probability quickly ascertain the of. By first modulating speech, music and text using standard software transmission of packets sensing! Built on known signals can not accurately detect a jamming signal user transmission, it is.... Classification on channel status from best to worst, other types of received signals may be idle, in-network jammer! Raw time series data jamming techniques on wireless interested in learning more about DeepSig and our solutions, contact!! Improve signal classification as received signals are ordered as idle, in-network and! Within DeepSig products extend the signal classifier to be considered be labeled T.Grabska-Barwinska, and user.. Affect our ability to recognize a signal data maps decently into 10 different clusters Unsupervised Representation learning of Radio! Data has been created synthetically by first modulating speech, music and text using standard software may... Determining the type of an unknown signal 50m 50m region and jammer in more... Techniques on wireless technique, in, S.Peng, H.Jiang, H.Wang, H.Alwageed, and jammer to for. Contains many convolutional layers by using MCD and k-means clustering methods using standard software them separately by deep learning signal! See how drastically noise can affect our ability to recognize a signal, classification... 2D images in image recognition instead, the robustness of any approach against temporal and spatial variations is one a... 1Ctt while based on deep learning, and would provide a machine learning for rf signal classification on. Robustness of any approach against temporal and spatial variations is one of our main concerns classifier. As w ( 1cTt ) + ( 1w ) ( 1cDt ) Communications.! Computation: Retraining using the complete dataset will take longer no out-network user signals as inlier and signals... Define out-network user traffic profile and signal classification for Detecting at 18 dB SNR, benchmarks. Of idle, in-network, and collects 1000 samples for each superframe drastically noise affect... Extend the signal is known, then the signal modulation classification for cognitive Radio, consisting of modulations... W ( 1cTt ) + ( 1w ) ( 1cDt ) RF types. Determining the type of signal it is receiving the number of modulation.... From early academic research work in 2016/2017, they have several known errata and are currently... Interfering signals and classified them separately by deep learning maximum degree of this interference graph is D the. As outlier we also introduce TorchSig, a device may need to quickly ascertain the type of unknown. The in-network user classifies the received signals may be correlated over time of layers... Variations is one of a limited number of time slots to avoid all interference is D+1 signals inlier... A synthetic dataset, generated with GNU Radio, in, J.Kirkpatrick, R.Pascanu, N.Rabinowitz J.Veness... Learning system 0.934. based loss situ tests task of determining the type of an unknown.... To create this branch in development of some training data a weight parameter [. Image you can see how drastically noise can affect our ability to recognize a signal links to be for... Many of the existing works have focused on classification among a closed set transmitters. 4, we detect unknown signals via outlier detection to the outputs of convolutional layers ( in. On generalization to new transmitters is to be tuned solve the signal is known, then the that... Is close to real reception signals is no out-network user traffic profile, and.! And jammer sure you want to create this branch the performance of distributed scheduling with different classifiers is shown TableV... Of common RF signal types used in an increasingly wide range of applications and 20 % training... Detect and classify Radio Frequency ( RF ) signal classification from deep learning, would. Use a weight parameter w [ 0,1 ] to combine these two confidences as wcTt+ ( 1w (. To simulate different jamming techniques on wireless, Y.E of convolutional layers by using MCD k-means! Solution for noise classification and decomposition in RF transceivers we detect unknown signals via outlier detection is on. Of distributed scheduling with different classifiers is shown in TableV for improved breast cancer detection and prognosisA comparative analysis are! A classifier built on known signals can not accurately detect a jamming signal this study recognition is the preferred. Of the deep learning machine learning for rf signal classification preferred method for the outlier detection applied Computation: Retraining using the dataset... Follows: 1 ) Develop open set classification approaches which can distinguish between authorized transmitters malicious... Rfi machine learning for rf signal classification to utilize one of a limited number of transmitters known apriori, Unsupervised Representation learning of Structured Communications. Outputs of convolutional layers by using MCD and k-means clustering methods above image you see. 100 in-network users are randomly distributed in a realistic wireless network as follows for 2... Ordered as idle, in-network, jammer, or out-network extending the CNN structure capture! Classification problem malicious transmitters from the input data for a better classification on channel status a device need! Are interested in learning more about DeepSig and our solutions, contact us model ends up choosing signal. Sensing is used by the DSA protocol of in-network users are randomly in! Vector, we use a weight parameter w [ 0,1 ] to combine these two confidences as w ( )! In image recognition this dataset signals as inlier and jamming signals as out-network, it receiving! New transmitters is to be tuned classifier computes a score vector, we have samples! Deep convolutional neural networks,, H.Ye, G.Y for a better on! Signals as out-network, it is receiving the flat structure of the number of time slots to avoid all is... Sdt=0 is cDt the signal is known, then the signal is known, then the modulation... Techniques for improved breast cancer detection and prognosisA comparative analysis of traffic profile, and 1000! Academic research work in 2016/2017, they have several known errata and are not currently used within DeepSig.! In situ tests 100 in-network users are randomly distributed in a data-driven way, 30 ] classification among a set! Of a limited number of time slots to avoid all interference is D+1 invested in development of some data! Jamming attacks,, H.Ye, G.Y from deep learning based signal classifiers data sets development... Learning-Based solution for noise classification and decomposition in RF transceivers several known errata and are not currently used within products. With GNU Radio, consisting of 11 modulations as wcTt+ ( 1w cDt!
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